Are You Properly Recommending? Why Most Platforms Still Aren’t

Every second your users spend scrolling instead of watching is a second closer to churn. Your users don’t leave because your catalog isn’t big enough. They leave because they can’t find what matters to them fast enough, because you are not recommending properly.

If your “recommendation engine” is just age-based suggestions or generic trending rows, you’re not really recommending at all. Thus, the strength of your recommendation engine can make or break engagement, and therefore your business goals too.

The Illusion of Relevance

Too many services tick the personalization box without actually delivering it. They serve content by demographics (“Kids get cartoons”), popularity (“Everyone’s watching this”), or regionality (“Users in Spain like this.”). That’s not personalization. That’s broadcasting in disguise. It results in generic rows, irrelevant content, and frustrated users.

The reality is a maturity gap in recommendation strategies. Platforms stall at the early stages and convince themselves they’ve “solved” personalization, when in fact they’ve only scratched the surface:

• Generic Experience: One-size-fits-all layouts and content.
• Demographic Targeting: Slightly better, but still surface-level.
• Behavioral Triggers: A nudge here and there, based on last play or time of day.
• Dynamic Segmentation: True grouping by evolving usage.
• Personalization Profiles: Experiences tied to lifecycle moments like onboarding or upsell.
• 1:1 Orchestration: Fully adaptive, AI-driven recommendations.

If you’re not climbing this ladder, you’re not competing; you’re just hoping your users do the work of finding their next favorite title. You can learn more about how to climb this chart in our guidebook “Winning Attention Through Your Digital Experience.”

Recommendations Powered by Intelligence

What you need is a recommendation engine to close the gap. That’s why we developed Recommender, to help you move beyond demographics and popularity to context-rich, AI-driven recommendations that evolve with every user. With our solution, you’re not just deploying a recommender, you’re powering it with the full context of your audience and content.

With Recommender, we analyse histories, preferences, and device activity to keep viewers engaged by delivering the right content to the right user at the right time. And with our Segments tool, you can use dynamic audience groupings that feed better inputs into what you’re recommending, turning them into true engagement levers. 

It’s this combination that bridges the gap between personalization and intelligence, making recommendations adaptive, lifecycle-aware, and always relevant. The impact it can have on your business is clear:

• Engagement that lasts: Less time scrolling, more time playing.
• Retention that sticks: Users feel understood, not treated as averages.
• Revenue that grows: Underused catalog titles find the right audiences, boosting ROI.

Other recommendation engines operate in a vacuum. They don’t connect to your analytics, so they can’t prove impact. With DXI, things are different. Because we unify audience behavior, content performance, and engagement metrics, you can see how each recommendation directly ties to churn reduction, retention, or upselling.

If your recommender is still guessing, your users are slipping away. Recommendation is not about filling a row. It’s about closing the maturity gap between what you think you’re offering and what users actually experience.

With DXI, you don’t just recommend, you orchestrate experiences that grow engagement, boost retention, and maximize ROI.